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<h1 id="deeptabular-utils">deeptabular utils<a class="headerlink" href="#deeptabular-utils" title="Permanent link">&para;</a></h1>


<div class="doc doc-object doc-class">



<h2 id="pytorch_widedeep.utils.deeptabular_utils.LabelEncoder" class="doc doc-heading">
            <span class="doc doc-object-name doc-class-name">LabelEncoder</span>


<a href="#pytorch_widedeep.utils.deeptabular_utils.LabelEncoder" class="headerlink" title="Permanent link">&para;</a></h2>


    <div class="doc doc-contents first">


        <p>Label Encode categorical values for multiple columns at once</p>
<p><img alt="ℹ️" class="emojione" src="https://cdnjs.cloudflare.com/ajax/libs/emojione/2.2.7/assets/png/2139.png" title=":information_source:" /> <strong>NOTE</strong>:
LabelEncoder reserves 0 for <code>unseen</code> new categories. This is convenient
when defining the embedding layers, since we can just set padding idx to 0.</p>


<p><span class="doc-section-title">Parameters:</span></p>
    <table>
      <thead>
        <tr>
          <th>Name</th>
          <th>Type</th>
          <th>Description</th>
          <th>Default</th>
        </tr>
      </thead>
      <tbody>
          <tr class="doc-section-item">
            <td>
                <code>columns_to_encode</code>
            </td>
            <td>
                  <code><span title="pytorch_widedeep.wdtypes.Optional">Optional</span>[<span title="pytorch_widedeep.wdtypes.List">List</span>[str]]</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>List of strings containing the names of the columns to encode. If
<code>None</code> all columns of type <code>object</code> in the dataframe will be label
encoded.</p>
              </div>
            </td>
            <td>
                  <code>None</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>with_attention</code>
            </td>
            <td>
                  <code>bool</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>Boolean indicating whether the preprocessed data will be passed to an
attention-based model. Aliased as <code>for_transformer</code>.</p>
              </div>
            </td>
            <td>
                  <code>False</code>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td>
                <code>shared_embed</code>
            </td>
            <td>
                  <code>bool</code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>Boolean indicating if the embeddings will be "<em>shared</em>" when using
attention-based models. The idea behind <code>shared_embed</code> is described
in the Appendix A in the <a href="https://arxiv.org/abs/2012.06678">TabTransformer paper</a>:
'<em>The goal of having column embedding is to enable the model to
distinguish the classes in one column from those in the
other columns</em>'. In other words, the idea is to let the model learn
which column is embedded at the time. See: <code>pytorch_widedeep.models.transformers._layers.SharedEmbeddings</code>.</p>
              </div>
            </td>
            <td>
                  <code>False</code>
            </td>
          </tr>
      </tbody>
    </table>


<p><span class="doc-section-title">Attributes:</span></p>
    <table>
      <thead>
        <tr>
          <th>Name</th>
          <th>Type</th>
          <th>Description</th>
        </tr>
      </thead>
      <tbody>
          <tr class="doc-section-item">
            <td><code><span title="pytorch_widedeep.utils.deeptabular_utils.LabelEncoder.encoding_dict">encoding_dict</span></code></td>
            <td>
                  <code><span title="pytorch_widedeep.wdtypes.Dict">Dict</span></code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>Dictionary containing the encoding mappings in the format, e.g. : <br/>
<code>{'colname1': {'cat1': 1, 'cat2': 2, ...}, 'colname2': {'cat1': 1, 'cat2': 2, ...}, ...}</code></p>
              </div>
            </td>
          </tr>
          <tr class="doc-section-item">
            <td><code><span title="pytorch_widedeep.utils.deeptabular_utils.LabelEncoder.inverse_encoding_dict">inverse_encoding_dict</span></code></td>
            <td>
                  <code><span title="pytorch_widedeep.wdtypes.Dict">Dict</span></code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>Dictionary containing the inverse encoding mappings in the format, e.g. : <br/>
<code>{'colname1': {1: 'cat1', 2: 'cat2', ...}, 'colname2': {1: 'cat1', 2: 'cat2', ...}, ...}</code></p>
              </div>
            </td>
          </tr>
      </tbody>
    </table>






              <details class="quote">
                <summary>Source code in <code>pytorch_widedeep/utils/deeptabular_utils.py</code></summary>
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<span class="normal">257</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">class</span> <span class="nc">LabelEncoder</span><span class="p">:</span>
<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Label Encode categorical values for multiple columns at once</span>

<span class="sd">    :information_source: **NOTE**:</span>
<span class="sd">    LabelEncoder reserves 0 for `unseen` new categories. This is convenient</span>
<span class="sd">    when defining the embedding layers, since we can just set padding idx to 0.</span>

<span class="sd">    Parameters</span>
<span class="sd">    ----------</span>
<span class="sd">    columns_to_encode: list, Optional, default = None</span>
<span class="sd">        List of strings containing the names of the columns to encode. If</span>
<span class="sd">        `None` all columns of type `object` in the dataframe will be label</span>
<span class="sd">        encoded.</span>
<span class="sd">    with_attention: bool, default = False</span>
<span class="sd">        Boolean indicating whether the preprocessed data will be passed to an</span>
<span class="sd">        attention-based model. Aliased as `for_transformer`.</span>
<span class="sd">    shared_embed: bool, default = False</span>
<span class="sd">        Boolean indicating if the embeddings will be &quot;_shared_&quot; when using</span>
<span class="sd">        attention-based models. The idea behind `shared_embed` is described</span>
<span class="sd">        in the Appendix A in the [TabTransformer paper](https://arxiv.org/abs/2012.06678):</span>
<span class="sd">        &#39;_The goal of having column embedding is to enable the model to</span>
<span class="sd">        distinguish the classes in one column from those in the</span>
<span class="sd">        other columns_&#39;. In other words, the idea is to let the model learn</span>
<span class="sd">        which column is embedded at the time. See: `pytorch_widedeep.models.transformers._layers.SharedEmbeddings`.</span>

<span class="sd">    Attributes</span>
<span class="sd">    ----------</span>
<span class="sd">    encoding_dict : Dict</span>
<span class="sd">        Dictionary containing the encoding mappings in the format, e.g. : &lt;br/&gt;</span>
<span class="sd">        `{&#39;colname1&#39;: {&#39;cat1&#39;: 1, &#39;cat2&#39;: 2, ...}, &#39;colname2&#39;: {&#39;cat1&#39;: 1, &#39;cat2&#39;: 2, ...}, ...}`</span>
<span class="sd">    inverse_encoding_dict : Dict</span>
<span class="sd">        Dictionary containing the inverse encoding mappings in the format, e.g. : &lt;br/&gt;</span>
<span class="sd">        `{&#39;colname1&#39;: {1: &#39;cat1&#39;, 2: &#39;cat2&#39;, ...}, &#39;colname2&#39;: {1: &#39;cat1&#39;, 2: &#39;cat2&#39;, ...}, ...}`</span>

<span class="sd">    &quot;&quot;&quot;</span>

    <span class="nd">@alias</span><span class="p">(</span><span class="s2">&quot;with_attention&quot;</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;for_transformer&quot;</span><span class="p">])</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
        <span class="bp">self</span><span class="p">,</span>
        <span class="n">columns_to_encode</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
        <span class="n">with_attention</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
        <span class="n">shared_embed</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
    <span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span> <span class="o">=</span> <span class="n">columns_to_encode</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">shared_embed</span> <span class="o">=</span> <span class="n">shared_embed</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">with_attention</span> <span class="o">=</span> <span class="n">with_attention</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">reset_embed_idx</span> <span class="o">=</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">with_attention</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">shared_embed</span>

    <span class="k">def</span> <span class="nf">partial_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;LabelEncoder&quot;</span><span class="p">:</span>  <span class="c1"># noqa: C901</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;Main method. Creates encoding attributes.</span>

<span class="sd">        Returns</span>
<span class="sd">        -------</span>
<span class="sd">        LabelEncoder</span>
<span class="sd">            `LabelEncoder` fitted object</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="c1"># here df is a chunk of the data. this is meant to be run when the</span>
        <span class="c1"># data is large and we pass a chunk at a time. Therefore, we do not</span>
        <span class="c1"># copy the input chunk as mutating a chunk is ok</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">select_dtypes</span><span class="p">(</span><span class="n">include</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;object&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="c1"># sanity check to make sure all categorical columns are in an adequate</span>
            <span class="c1"># format</span>
            <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span><span class="p">:</span>
                <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;O&quot;</span><span class="p">)</span>

        <span class="n">unique_column_vals</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="p">{}</span>
        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span><span class="p">:</span>
            <span class="n">unique_column_vals</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>

        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;encoding_dict&quot;</span><span class="p">):</span>
            <span class="c1"># we run the method &#39;partial_fit&#39; for the 1st time</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">int</span><span class="p">]]</span> <span class="o">=</span> <span class="p">{}</span>
            <span class="k">if</span> <span class="s2">&quot;cls_token&quot;</span> <span class="ow">in</span> <span class="n">unique_column_vals</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">shared_embed</span><span class="p">:</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="p">[</span><span class="s2">&quot;cls_token&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;[CLS]&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">}</span>
                <span class="k">del</span> <span class="n">unique_column_vals</span><span class="p">[</span><span class="s2">&quot;cls_token&quot;</span><span class="p">]</span>

            <span class="c1"># leave 0 for padding/&quot;unseen&quot; categories. Also we need an</span>
            <span class="c1"># attribute to keep track of the encoding in case we use</span>
            <span class="c1"># attention and we do not re-start the index/counter</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">cum_idx</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span>
            <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">unique_column_vals</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="n">o</span><span class="p">:</span> <span class="n">i</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">cum_idx</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">o</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">v</span><span class="p">)}</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">cum_idx</span> <span class="o">=</span> <span class="mi">1</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reset_embed_idx</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">cum_idx</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="c1"># the &#39;partial_fit&#39; method has already run.</span>
            <span class="c1"># &quot;cls_token&quot; will have been added already</span>
            <span class="k">if</span> <span class="s2">&quot;cls_token&quot;</span> <span class="ow">in</span> <span class="n">unique_column_vals</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">shared_embed</span><span class="p">:</span>
                <span class="k">del</span> <span class="n">unique_column_vals</span><span class="p">[</span><span class="s2">&quot;cls_token&quot;</span><span class="p">]</span>

            <span class="c1"># Classes in the new df/chunk of the dataset that have not been seen</span>
            <span class="c1"># before</span>
            <span class="n">unseen_classes</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="p">{}</span>
            <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span><span class="p">:</span>
                <span class="n">unseen_classes</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span>
                    <span class="n">np</span><span class="o">.</span><span class="n">setdiff1d</span><span class="p">(</span>
                        <span class="n">unique_column_vals</span><span class="p">[</span><span class="n">c</span><span class="p">],</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
                    <span class="p">)</span>
                <span class="p">)</span>

            <span class="c1"># leave 0 for padding/&quot;unseen&quot; categories</span>
            <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">unique_column_vals</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
                <span class="c1"># if we use attention we need to start encoding from the</span>
                <span class="c1"># last &#39;overall&#39; encoding index. Otherwise, we use the max</span>
                <span class="c1"># encoding index per categorical col</span>
                <span class="n">_idx</span> <span class="o">=</span> <span class="p">(</span>
                    <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">())</span> <span class="o">+</span> <span class="mi">1</span>
                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reset_embed_idx</span>
                    <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">cum_idx</span>
                <span class="p">)</span>
                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">unseen_classes</span><span class="p">[</span><span class="n">k</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
                    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">o</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">unseen_classes</span><span class="p">[</span><span class="n">k</span><span class="p">]):</span>
                        <span class="k">if</span> <span class="n">o</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="p">[</span><span class="n">k</span><span class="p">]:</span>
                            <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="p">[</span><span class="n">k</span><span class="p">][</span><span class="n">o</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span> <span class="o">+</span> <span class="n">_idx</span>
                    <span class="c1"># if self.reset_embed_idx is True it will be 1 anyway</span>
                    <span class="bp">self</span><span class="o">.</span><span class="n">cum_idx</span> <span class="o">=</span> <span class="p">(</span>
                        <span class="mi">1</span>
                        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reset_embed_idx</span>
                        <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">cum_idx</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">unseen_classes</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
                    <span class="p">)</span>

        <span class="k">return</span> <span class="bp">self</span>

    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;LabelEncoder&quot;</span><span class="p">:</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;Simply runs the `partial_fit` method when the data fits in memory</span>

<span class="sd">        Returns</span>
<span class="sd">        -------</span>
<span class="sd">        LabelEncoder</span>
<span class="sd">            `LabelEncoder` fitted object</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="c1"># this is meant to be run when the data fits in memory and therefore,</span>
        <span class="c1"># we do not want to mutate the original df, so we copy it</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">())</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">inverse_encoding_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_inverse_encoding_dict</span><span class="p">()</span>

        <span class="k">return</span> <span class="bp">self</span>

    <span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;Label Encoded the categories in `columns_to_encode`</span>

<span class="sd">        Returns</span>
<span class="sd">        -------</span>
<span class="sd">        pd.DataFrame</span>
<span class="sd">            label-encoded dataframe</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">try</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span>
        <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
            <span class="k">raise</span> <span class="n">NotFittedError</span><span class="p">(</span>
                <span class="s2">&quot;This LabelEncoder instance is not fitted yet. &quot;</span>
                <span class="s2">&quot;Call &#39;fit&#39; with appropriate arguments before using this LabelEncoder.&quot;</span>
            <span class="p">)</span>

        <span class="n">df_inp</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
        <span class="c1"># sanity check to make sure all categorical columns are in an adequate</span>
        <span class="c1"># format</span>
        <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span><span class="p">:</span>  <span class="c1"># type: ignore</span>
            <span class="n">df_inp</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_inp</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;O&quot;</span><span class="p">)</span>

        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
            <span class="n">df_inp</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_inp</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">v</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">if</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">v</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>

        <span class="k">return</span> <span class="n">df_inp</span>

    <span class="k">def</span> <span class="nf">fit_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;Combines `fit` and `transform`</span>

<span class="sd">        Examples</span>
<span class="sd">        --------</span>

<span class="sd">        &gt;&gt;&gt; import pandas as pd</span>
<span class="sd">        &gt;&gt;&gt; from pytorch_widedeep.utils import LabelEncoder</span>
<span class="sd">        &gt;&gt;&gt; df = pd.DataFrame({&#39;col1&#39;: [1,2,3], &#39;col2&#39;: [&#39;me&#39;, &#39;you&#39;, &#39;him&#39;]})</span>
<span class="sd">        &gt;&gt;&gt; columns_to_encode = [&#39;col2&#39;]</span>
<span class="sd">        &gt;&gt;&gt; encoder = LabelEncoder(columns_to_encode)</span>
<span class="sd">        &gt;&gt;&gt; encoder.fit_transform(df)</span>
<span class="sd">           col1  col2</span>
<span class="sd">        0     1     1</span>
<span class="sd">        1     2     2</span>
<span class="sd">        2     3     3</span>
<span class="sd">        &gt;&gt;&gt; encoder.encoding_dict</span>
<span class="sd">        {&#39;col2&#39;: {&#39;me&#39;: 1, &#39;you&#39;: 2, &#39;him&#39;: 3}}</span>

<span class="sd">        Returns</span>
<span class="sd">        -------</span>
<span class="sd">        pd.DataFrame</span>
<span class="sd">            label-encoded dataframe</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">create_inverse_encoding_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]:</span>
        <span class="n">inverse_encoding_dict</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">()</span>
        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="p">:</span>
            <span class="n">inverse_encoding_dict</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="n">v</span><span class="p">:</span> <span class="n">k</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">()}</span>
            <span class="n">inverse_encoding_dict</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;unseen&quot;</span>
        <span class="k">return</span> <span class="n">inverse_encoding_dict</span>

    <span class="k">def</span> <span class="nf">inverse_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns the original categories</span>

<span class="sd">        Examples</span>
<span class="sd">        --------</span>

<span class="sd">        &gt;&gt;&gt; import pandas as pd</span>
<span class="sd">        &gt;&gt;&gt; from pytorch_widedeep.utils import LabelEncoder</span>
<span class="sd">        &gt;&gt;&gt; df = pd.DataFrame({&#39;col1&#39;: [1,2,3], &#39;col2&#39;: [&#39;me&#39;, &#39;you&#39;, &#39;him&#39;]})</span>
<span class="sd">        &gt;&gt;&gt; columns_to_encode = [&#39;col2&#39;]</span>
<span class="sd">        &gt;&gt;&gt; encoder = LabelEncoder(columns_to_encode)</span>
<span class="sd">        &gt;&gt;&gt; df_enc = encoder.fit_transform(df)</span>
<span class="sd">        &gt;&gt;&gt; encoder.inverse_transform(df_enc)</span>
<span class="sd">           col1 col2</span>
<span class="sd">        0     1   me</span>
<span class="sd">        1     2  you</span>
<span class="sd">        2     3  him</span>

<span class="sd">        Returns</span>
<span class="sd">        -------</span>
<span class="sd">        pd.DataFrame</span>
<span class="sd">            DataFrame with original categories</span>
<span class="sd">        &quot;&quot;&quot;</span>

        <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;inverse_encoding_dict&quot;</span><span class="p">):</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">inverse_encoding_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_inverse_encoding_dict</span><span class="p">()</span>

        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">inverse_encoding_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
            <span class="n">df</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">v</span><span class="p">[</span><span class="n">x</span><span class="p">])</span>

        <span class="k">return</span> <span class="n">df</span>

    <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
        <span class="n">list_of_params</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">list_of_params</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">&quot;columns_to_encode=</span><span class="si">{columns_to_encode}</span><span class="s2">&quot;</span><span class="p">)</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">with_attention</span><span class="p">:</span>
            <span class="n">list_of_params</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">&quot;with_attention=</span><span class="si">{with_attention}</span><span class="s2">&quot;</span><span class="p">)</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">shared_embed</span><span class="p">:</span>
            <span class="n">list_of_params</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">&quot;shared_embed=</span><span class="si">{shared_embed}</span><span class="s2">&quot;</span><span class="p">)</span>
        <span class="n">all_params</span> <span class="o">=</span> <span class="s2">&quot;, &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">list_of_params</span><span class="p">)</span>
        <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;LabelEncoder(</span><span class="si">{</span><span class="n">all_params</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">)</span><span class="si">}</span><span class="s2">)&quot;</span>
</code></pre></div></td></tr></table></div>
              </details>



  <div class="doc doc-children">









<div class="doc doc-object doc-function">


<h3 id="pytorch_widedeep.utils.deeptabular_utils.LabelEncoder.partial_fit" class="doc doc-heading">
            <span class="doc doc-object-name doc-function-name">partial_fit</span>


<a href="#pytorch_widedeep.utils.deeptabular_utils.LabelEncoder.partial_fit" class="headerlink" title="Permanent link">&para;</a></h3>
<div class="doc-signature highlight"><pre><span></span><code><span class="nf">partial_fit</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
</code></pre></div>

    <div class="doc doc-contents ">

        <p>Main method. Creates encoding attributes.</p>


    <p><span class="doc-section-title">Returns:</span></p>
    <table>
      <thead>
        <tr>
          <th>Type</th>
          <th>Description</th>
        </tr>
      </thead>
      <tbody>
          <tr class="doc-section-item">
            <td>
                  <code><a class="autorefs autorefs-internal" title="pytorch_widedeep.utils.deeptabular_utils.LabelEncoder" href="#pytorch_widedeep.utils.deeptabular_utils.LabelEncoder">LabelEncoder</a></code>
            </td>
            <td>
              <div class="doc-md-description">
                <p><code>LabelEncoder</code> fitted object</p>
              </div>
            </td>
          </tr>
      </tbody>
    </table>

            <details class="quote">
              <summary>Source code in <code>pytorch_widedeep/utils/deeptabular_utils.py</code></summary>
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<span class="normal">138</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">partial_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;LabelEncoder&quot;</span><span class="p">:</span>  <span class="c1"># noqa: C901</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;Main method. Creates encoding attributes.</span>

<span class="sd">    Returns</span>
<span class="sd">    -------</span>
<span class="sd">    LabelEncoder</span>
<span class="sd">        `LabelEncoder` fitted object</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="c1"># here df is a chunk of the data. this is meant to be run when the</span>
    <span class="c1"># data is large and we pass a chunk at a time. Therefore, we do not</span>
    <span class="c1"># copy the input chunk as mutating a chunk is ok</span>
    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">select_dtypes</span><span class="p">(</span><span class="n">include</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;object&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="c1"># sanity check to make sure all categorical columns are in an adequate</span>
        <span class="c1"># format</span>
        <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span><span class="p">:</span>
            <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;O&quot;</span><span class="p">)</span>

    <span class="n">unique_column_vals</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="p">{}</span>
    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span><span class="p">:</span>
        <span class="n">unique_column_vals</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>

    <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;encoding_dict&quot;</span><span class="p">):</span>
        <span class="c1"># we run the method &#39;partial_fit&#39; for the 1st time</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">int</span><span class="p">]]</span> <span class="o">=</span> <span class="p">{}</span>
        <span class="k">if</span> <span class="s2">&quot;cls_token&quot;</span> <span class="ow">in</span> <span class="n">unique_column_vals</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">shared_embed</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="p">[</span><span class="s2">&quot;cls_token&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;[CLS]&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">}</span>
            <span class="k">del</span> <span class="n">unique_column_vals</span><span class="p">[</span><span class="s2">&quot;cls_token&quot;</span><span class="p">]</span>

        <span class="c1"># leave 0 for padding/&quot;unseen&quot; categories. Also we need an</span>
        <span class="c1"># attribute to keep track of the encoding in case we use</span>
        <span class="c1"># attention and we do not re-start the index/counter</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">cum_idx</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span>
        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">unique_column_vals</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="n">o</span><span class="p">:</span> <span class="n">i</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">cum_idx</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">o</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">v</span><span class="p">)}</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">cum_idx</span> <span class="o">=</span> <span class="mi">1</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reset_embed_idx</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">cum_idx</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
    <span class="k">else</span><span class="p">:</span>
        <span class="c1"># the &#39;partial_fit&#39; method has already run.</span>
        <span class="c1"># &quot;cls_token&quot; will have been added already</span>
        <span class="k">if</span> <span class="s2">&quot;cls_token&quot;</span> <span class="ow">in</span> <span class="n">unique_column_vals</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">shared_embed</span><span class="p">:</span>
            <span class="k">del</span> <span class="n">unique_column_vals</span><span class="p">[</span><span class="s2">&quot;cls_token&quot;</span><span class="p">]</span>

        <span class="c1"># Classes in the new df/chunk of the dataset that have not been seen</span>
        <span class="c1"># before</span>
        <span class="n">unseen_classes</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="p">{}</span>
        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span><span class="p">:</span>
            <span class="n">unseen_classes</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span>
                <span class="n">np</span><span class="o">.</span><span class="n">setdiff1d</span><span class="p">(</span>
                    <span class="n">unique_column_vals</span><span class="p">[</span><span class="n">c</span><span class="p">],</span> <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
                <span class="p">)</span>
            <span class="p">)</span>

        <span class="c1"># leave 0 for padding/&quot;unseen&quot; categories</span>
        <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">unique_column_vals</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
            <span class="c1"># if we use attention we need to start encoding from the</span>
            <span class="c1"># last &#39;overall&#39; encoding index. Otherwise, we use the max</span>
            <span class="c1"># encoding index per categorical col</span>
            <span class="n">_idx</span> <span class="o">=</span> <span class="p">(</span>
                <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">())</span> <span class="o">+</span> <span class="mi">1</span>
                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reset_embed_idx</span>
                <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">cum_idx</span>
            <span class="p">)</span>
            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">unseen_classes</span><span class="p">[</span><span class="n">k</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">o</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">unseen_classes</span><span class="p">[</span><span class="n">k</span><span class="p">]):</span>
                    <span class="k">if</span> <span class="n">o</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="p">[</span><span class="n">k</span><span class="p">]:</span>
                        <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="p">[</span><span class="n">k</span><span class="p">][</span><span class="n">o</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span> <span class="o">+</span> <span class="n">_idx</span>
                <span class="c1"># if self.reset_embed_idx is True it will be 1 anyway</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">cum_idx</span> <span class="o">=</span> <span class="p">(</span>
                    <span class="mi">1</span>
                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reset_embed_idx</span>
                    <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">cum_idx</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">unseen_classes</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
                <span class="p">)</span>

    <span class="k">return</span> <span class="bp">self</span>
</code></pre></div></td></tr></table></div>
            </details>
    </div>

</div>

<div class="doc doc-object doc-function">


<h3 id="pytorch_widedeep.utils.deeptabular_utils.LabelEncoder.fit" class="doc doc-heading">
            <span class="doc doc-object-name doc-function-name">fit</span>


<a href="#pytorch_widedeep.utils.deeptabular_utils.LabelEncoder.fit" class="headerlink" title="Permanent link">&para;</a></h3>
<div class="doc-signature highlight"><pre><span></span><code><span class="nf">fit</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
</code></pre></div>

    <div class="doc doc-contents ">

        <p>Simply runs the <code>partial_fit</code> method when the data fits in memory</p>


    <p><span class="doc-section-title">Returns:</span></p>
    <table>
      <thead>
        <tr>
          <th>Type</th>
          <th>Description</th>
        </tr>
      </thead>
      <tbody>
          <tr class="doc-section-item">
            <td>
                  <code><a class="autorefs autorefs-internal" title="pytorch_widedeep.utils.deeptabular_utils.LabelEncoder" href="#pytorch_widedeep.utils.deeptabular_utils.LabelEncoder">LabelEncoder</a></code>
            </td>
            <td>
              <div class="doc-md-description">
                <p><code>LabelEncoder</code> fitted object</p>
              </div>
            </td>
          </tr>
      </tbody>
    </table>

            <details class="quote">
              <summary>Source code in <code>pytorch_widedeep/utils/deeptabular_utils.py</code></summary>
              <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">140</span>
<span class="normal">141</span>
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<span class="normal">154</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;LabelEncoder&quot;</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;Simply runs the `partial_fit` method when the data fits in memory</span>

<span class="sd">    Returns</span>
<span class="sd">    -------</span>
<span class="sd">    LabelEncoder</span>
<span class="sd">        `LabelEncoder` fitted object</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="c1"># this is meant to be run when the data fits in memory and therefore,</span>
    <span class="c1"># we do not want to mutate the original df, so we copy it</span>
    <span class="bp">self</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">())</span>

    <span class="bp">self</span><span class="o">.</span><span class="n">inverse_encoding_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_inverse_encoding_dict</span><span class="p">()</span>

    <span class="k">return</span> <span class="bp">self</span>
</code></pre></div></td></tr></table></div>
            </details>
    </div>

</div>

<div class="doc doc-object doc-function">


<h3 id="pytorch_widedeep.utils.deeptabular_utils.LabelEncoder.transform" class="doc doc-heading">
            <span class="doc doc-object-name doc-function-name">transform</span>


<a href="#pytorch_widedeep.utils.deeptabular_utils.LabelEncoder.transform" class="headerlink" title="Permanent link">&para;</a></h3>
<div class="doc-signature highlight"><pre><span></span><code><span class="nf">transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
</code></pre></div>

    <div class="doc doc-contents ">

        <p>Label Encoded the categories in <code>columns_to_encode</code></p>


    <p><span class="doc-section-title">Returns:</span></p>
    <table>
      <thead>
        <tr>
          <th>Type</th>
          <th>Description</th>
        </tr>
      </thead>
      <tbody>
          <tr class="doc-section-item">
            <td>
                  <code><span title="pandas.DataFrame">DataFrame</span></code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>label-encoded dataframe</p>
              </div>
            </td>
          </tr>
      </tbody>
    </table>

            <details class="quote">
              <summary>Source code in <code>pytorch_widedeep/utils/deeptabular_utils.py</code></summary>
              <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">156</span>
<span class="normal">157</span>
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<span class="normal">181</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;Label Encoded the categories in `columns_to_encode`</span>

<span class="sd">    Returns</span>
<span class="sd">    -------</span>
<span class="sd">    pd.DataFrame</span>
<span class="sd">        label-encoded dataframe</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">try</span><span class="p">:</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span>
    <span class="k">except</span> <span class="ne">AttributeError</span><span class="p">:</span>
        <span class="k">raise</span> <span class="n">NotFittedError</span><span class="p">(</span>
            <span class="s2">&quot;This LabelEncoder instance is not fitted yet. &quot;</span>
            <span class="s2">&quot;Call &#39;fit&#39; with appropriate arguments before using this LabelEncoder.&quot;</span>
        <span class="p">)</span>

    <span class="n">df_inp</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
    <span class="c1"># sanity check to make sure all categorical columns are in an adequate</span>
    <span class="c1"># format</span>
    <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">columns_to_encode</span><span class="p">:</span>  <span class="c1"># type: ignore</span>
        <span class="n">df_inp</span><span class="p">[</span><span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_inp</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;O&quot;</span><span class="p">)</span>

    <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">encoding_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
        <span class="n">df_inp</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">df_inp</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">v</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">if</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">v</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>

    <span class="k">return</span> <span class="n">df_inp</span>
</code></pre></div></td></tr></table></div>
            </details>
    </div>

</div>

<div class="doc doc-object doc-function">


<h3 id="pytorch_widedeep.utils.deeptabular_utils.LabelEncoder.fit_transform" class="doc doc-heading">
            <span class="doc doc-object-name doc-function-name">fit_transform</span>


<a href="#pytorch_widedeep.utils.deeptabular_utils.LabelEncoder.fit_transform" class="headerlink" title="Permanent link">&para;</a></h3>
<div class="doc-signature highlight"><pre><span></span><code><span class="nf">fit_transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
</code></pre></div>

    <div class="doc doc-contents ">

        <p>Combines <code>fit</code> and <code>transform</code></p>


<p><span class="doc-section-title">Examples:</span></p>
    <div class="highlight"><pre><span></span><code><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">pytorch_widedeep.utils</span> <span class="kn">import</span> <span class="n">LabelEncoder</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;col1&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="s1">&#39;col2&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;me&#39;</span><span class="p">,</span> <span class="s1">&#39;you&#39;</span><span class="p">,</span> <span class="s1">&#39;him&#39;</span><span class="p">]})</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">columns_to_encode</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;col2&#39;</span><span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">encoder</span> <span class="o">=</span> <span class="n">LabelEncoder</span><span class="p">(</span><span class="n">columns_to_encode</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">encoder</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
<span class="go">   col1  col2</span>
<span class="go">0     1     1</span>
<span class="go">1     2     2</span>
<span class="go">2     3     3</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">encoder</span><span class="o">.</span><span class="n">encoding_dict</span>
<span class="go">{&#39;col2&#39;: {&#39;me&#39;: 1, &#39;you&#39;: 2, &#39;him&#39;: 3}}</span>
</code></pre></div>


    <p><span class="doc-section-title">Returns:</span></p>
    <table>
      <thead>
        <tr>
          <th>Type</th>
          <th>Description</th>
        </tr>
      </thead>
      <tbody>
          <tr class="doc-section-item">
            <td>
                  <code><span title="pandas.DataFrame">DataFrame</span></code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>label-encoded dataframe</p>
              </div>
            </td>
          </tr>
      </tbody>
    </table>

            <details class="quote">
              <summary>Source code in <code>pytorch_widedeep/utils/deeptabular_utils.py</code></summary>
              <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">183</span>
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<span class="normal">207</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">fit_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;Combines `fit` and `transform`</span>

<span class="sd">    Examples</span>
<span class="sd">    --------</span>

<span class="sd">    &gt;&gt;&gt; import pandas as pd</span>
<span class="sd">    &gt;&gt;&gt; from pytorch_widedeep.utils import LabelEncoder</span>
<span class="sd">    &gt;&gt;&gt; df = pd.DataFrame({&#39;col1&#39;: [1,2,3], &#39;col2&#39;: [&#39;me&#39;, &#39;you&#39;, &#39;him&#39;]})</span>
<span class="sd">    &gt;&gt;&gt; columns_to_encode = [&#39;col2&#39;]</span>
<span class="sd">    &gt;&gt;&gt; encoder = LabelEncoder(columns_to_encode)</span>
<span class="sd">    &gt;&gt;&gt; encoder.fit_transform(df)</span>
<span class="sd">       col1  col2</span>
<span class="sd">    0     1     1</span>
<span class="sd">    1     2     2</span>
<span class="sd">    2     3     3</span>
<span class="sd">    &gt;&gt;&gt; encoder.encoding_dict</span>
<span class="sd">    {&#39;col2&#39;: {&#39;me&#39;: 1, &#39;you&#39;: 2, &#39;him&#39;: 3}}</span>

<span class="sd">    Returns</span>
<span class="sd">    -------</span>
<span class="sd">    pd.DataFrame</span>
<span class="sd">        label-encoded dataframe</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">df</span><span class="p">)</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
</code></pre></div></td></tr></table></div>
            </details>
    </div>

</div>

<div class="doc doc-object doc-function">


<h3 id="pytorch_widedeep.utils.deeptabular_utils.LabelEncoder.inverse_transform" class="doc doc-heading">
            <span class="doc doc-object-name doc-function-name">inverse_transform</span>


<a href="#pytorch_widedeep.utils.deeptabular_utils.LabelEncoder.inverse_transform" class="headerlink" title="Permanent link">&para;</a></h3>
<div class="doc-signature highlight"><pre><span></span><code><span class="nf">inverse_transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
</code></pre></div>

    <div class="doc doc-contents ">

        <p>Returns the original categories</p>


<p><span class="doc-section-title">Examples:</span></p>
    <div class="highlight"><pre><span></span><code><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">from</span> <span class="nn">pytorch_widedeep.utils</span> <span class="kn">import</span> <span class="n">LabelEncoder</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;col1&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="s1">&#39;col2&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;me&#39;</span><span class="p">,</span> <span class="s1">&#39;you&#39;</span><span class="p">,</span> <span class="s1">&#39;him&#39;</span><span class="p">]})</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">columns_to_encode</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;col2&#39;</span><span class="p">]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">encoder</span> <span class="o">=</span> <span class="n">LabelEncoder</span><span class="p">(</span><span class="n">columns_to_encode</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">df_enc</span> <span class="o">=</span> <span class="n">encoder</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">encoder</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">df_enc</span><span class="p">)</span>
<span class="go">   col1 col2</span>
<span class="go">0     1   me</span>
<span class="go">1     2  you</span>
<span class="go">2     3  him</span>
</code></pre></div>


    <p><span class="doc-section-title">Returns:</span></p>
    <table>
      <thead>
        <tr>
          <th>Type</th>
          <th>Description</th>
        </tr>
      </thead>
      <tbody>
          <tr class="doc-section-item">
            <td>
                  <code><span title="pandas.DataFrame">DataFrame</span></code>
            </td>
            <td>
              <div class="doc-md-description">
                <p>DataFrame with original categories</p>
              </div>
            </td>
          </tr>
      </tbody>
    </table>

            <details class="quote">
              <summary>Source code in <code>pytorch_widedeep/utils/deeptabular_utils.py</code></summary>
              <div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal">216</span>
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<span class="normal">244</span>
<span class="normal">245</span>
<span class="normal">246</span></pre></div></td><td class="code"><div><pre><span></span><code><span class="k">def</span> <span class="nf">inverse_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns the original categories</span>

<span class="sd">    Examples</span>
<span class="sd">    --------</span>

<span class="sd">    &gt;&gt;&gt; import pandas as pd</span>
<span class="sd">    &gt;&gt;&gt; from pytorch_widedeep.utils import LabelEncoder</span>
<span class="sd">    &gt;&gt;&gt; df = pd.DataFrame({&#39;col1&#39;: [1,2,3], &#39;col2&#39;: [&#39;me&#39;, &#39;you&#39;, &#39;him&#39;]})</span>
<span class="sd">    &gt;&gt;&gt; columns_to_encode = [&#39;col2&#39;]</span>
<span class="sd">    &gt;&gt;&gt; encoder = LabelEncoder(columns_to_encode)</span>
<span class="sd">    &gt;&gt;&gt; df_enc = encoder.fit_transform(df)</span>
<span class="sd">    &gt;&gt;&gt; encoder.inverse_transform(df_enc)</span>
<span class="sd">       col1 col2</span>
<span class="sd">    0     1   me</span>
<span class="sd">    1     2  you</span>
<span class="sd">    2     3  him</span>

<span class="sd">    Returns</span>
<span class="sd">    -------</span>
<span class="sd">    pd.DataFrame</span>
<span class="sd">        DataFrame with original categories</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;inverse_encoding_dict&quot;</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">inverse_encoding_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_inverse_encoding_dict</span><span class="p">()</span>

    <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">inverse_encoding_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
        <span class="n">df</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">v</span><span class="p">[</span><span class="n">x</span><span class="p">])</span>

    <span class="k">return</span> <span class="n">df</span>
</code></pre></div></td></tr></table></div>
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